Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "70" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 37 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 35 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2459852 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 8.456627 | -0.293421 | 0.865772 | -0.843292 | 3.496131 | 1.178002 | 0.315218 | -0.859387 | 0.8574 | 0.8570 | 0.2208 | 63.100137 | 53.514547 |
| 2459851 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 7.800919 | 0.008172 | -0.084279 | -0.442252 | 2.285607 | 0.667245 | 0.248455 | 0.252253 | 0.7908 | 0.7708 | 0.3198 | 21.865311 | 15.806733 |
| 2459850 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 8.374646 | -0.838765 | -0.166148 | -0.264930 | 3.684279 | 0.195774 | -0.248845 | 1.041498 | 0.7744 | 0.7803 | 0.3330 | 20.183388 | 14.304430 |
| 2459849 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 9.893644 | -0.951941 | 0.874587 | -0.114225 | 8.906623 | 0.423014 | 0.846833 | 0.187647 | 0.7721 | 0.7723 | 0.3391 | 21.550960 | 14.253741 |
| 2459848 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 9.323621 | -0.672141 | 0.086023 | -1.143933 | 2.696610 | 0.632249 | -0.314586 | 0.214673 | 0.7544 | 0.7739 | 0.3596 | 15.384548 | 10.906954 |
| 2459847 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 10.038729 | -0.532019 | 0.607088 | -0.801615 | 0.913102 | 0.664998 | -0.478782 | -0.218355 | 0.7544 | 0.7121 | 0.4135 | 13.817565 | 10.477509 |
| 2459846 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 9.578967 | -1.745728 | 0.553534 | -0.051104 | 3.132225 | 2.544959 | 0.002540 | 2.639137 | 0.8586 | 0.6838 | 0.4561 | 14.942183 | 8.146350 |
| 2459845 | RF_maintenance | 100.00% | 0.00% | 0.55% | 0.00% | 100.00% | 0.00% | 11.820838 | -1.290675 | 0.268154 | -0.118307 | 12.502613 | 0.840959 | 0.673972 | 2.232043 | 0.7592 | 0.7566 | 0.3686 | 7.337793 | 4.915965 |
| 2459844 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 60.469304 | 50.467234 | 97.760971 | 103.394563 | 310.875066 | 227.092493 | 48.965997 | 63.044520 | 0.8892 | 0.6140 | 0.5779 | nan | nan |
| 2459843 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 8.895978 | -0.252056 | 0.098134 | -0.977040 | 73.978875 | 81.109984 | 0.964184 | 0.685384 | 0.7733 | 0.7721 | 0.3783 | 15.184850 | 11.835276 |
| 2459840 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 59.868823 | -0.646397 | 15.423634 | -0.389525 | 9.168082 | 0.158602 | 22.480464 | 0.569161 | 0.0196 | 0.0231 | 0.0012 | nan | nan |
| 2459839 | RF_maintenance | 100.00% | - | - | - | - | - | 15.189952 | -1.114608 | 36.721142 | -0.432942 | 2.622069 | -1.438023 | 31.920818 | 0.786408 | nan | nan | nan | nan | nan |
| 2459838 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 80.276665 | 95.009380 | 83.557078 | 64.375493 | 109.401472 | 60.179840 | 1152.780386 | 367.310189 | 0.0164 | 0.0164 | 0.0003 | 1.046017 | 1.044725 |
| 2459836 | RF_maintenance | - | 0.00% | 16.11% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.6505 | 0.5280 | 0.4017 | nan | nan |
| 2459835 | RF_maintenance | 100.00% | 0.00% | 83.33% | 0.00% | - | - | 5.715348 | 5.348576 | 9.445975 | 10.874766 | 48.368973 | 23.798494 | -1.700472 | -4.319967 | 0.7219 | 0.3707 | 0.5572 | nan | nan |
| 2459833 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 22.436812 | 19.046372 | 32.547052 | 29.594530 | 330.441792 | 316.639465 | 31.124191 | 41.956436 | 0.7412 | 0.4537 | 0.5147 | nan | nan |
| 2459832 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 11.622349 | -1.751379 | 1.982080 | -0.870516 | 3.747527 | 3.292810 | 1.244831 | 4.685262 | 0.0779 | 0.0789 | 0.0123 | 1.237561 | 1.230220 |
| 2459831 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 15.674380 | 0.602958 | 39.654066 | -0.598132 | 2.723431 | -1.173532 | 23.496090 | 0.182202 | 0.0206 | 0.0401 | 0.0066 | nan | nan |
| 2459830 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 11.311974 | -1.756946 | 1.161567 | -1.114440 | 1.352190 | 0.776556 | 0.031375 | 2.720704 | 0.0713 | 0.0675 | 0.0086 | 1.262702 | 1.263470 |
| 2459829 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 14.999299 | -2.182727 | -0.266502 | -0.834072 | 10.225175 | 1.853197 | 1.766844 | 3.442189 | 0.0747 | 0.0685 | 0.0083 | 0.934302 | 0.924239 |
| 2459828 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 10.352550 | -1.088280 | -0.199719 | -0.480246 | 0.952293 | 2.344704 | 0.077749 | 12.159684 | 0.0789 | 0.0702 | 0.0108 | 1.258343 | 1.251926 |
| 2459827 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 11.764318 | -1.454700 | 0.611002 | -0.968971 | 2.609994 | 2.265415 | -0.281667 | -0.118386 | 0.0730 | 0.0721 | 0.0085 | 1.235505 | 1.235236 |
| 2459826 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 8.596313 | -0.987261 | 0.824853 | -0.616144 | 3.393499 | -0.999831 | 1.135454 | 6.851849 | 0.0706 | 0.0561 | 0.0062 | 1.228996 | 1.227665 |
| 2459825 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 9.140665 | -1.248403 | 1.045872 | -0.992336 | -0.908615 | 0.857089 | -0.819655 | -0.607561 | 0.0839 | 0.0735 | 0.0113 | 1.101997 | 1.106826 |
| 2459824 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 9.340714 | -1.812851 | 0.779806 | -0.986517 | 2.317545 | -0.906943 | 0.230268 | 0.860818 | 0.0767 | 0.0771 | 0.0096 | 1.200873 | 1.199740 |
| 2459823 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 7.203718 | -1.398972 | 1.485308 | -0.452326 | 0.479001 | -0.939514 | -0.679824 | 1.893036 | 0.0805 | 0.0756 | 0.0098 | 1.182393 | 1.195141 |
| 2459822 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 8.841712 | -1.063345 | 0.474672 | -1.021649 | -0.656194 | -1.243257 | 0.627963 | -0.218445 | 0.0892 | 0.0830 | 0.0091 | 1.177698 | 1.177539 |
| 2459821 | RF_maintenance | 100.00% | 11.29% | 11.29% | 0.00% | 100.00% | 0.00% | 9.925905 | -0.946732 | 0.373186 | -1.024131 | -1.203158 | -0.961556 | 1.661898 | -0.566188 | 0.7342 | 0.6197 | 0.4175 | 14.010384 | 10.543052 |
| 2459820 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 11.353067 | -1.473501 | -0.124232 | -0.860073 | 20.355922 | 4.018431 | 1.242641 | 0.764547 | 0.7950 | 0.7198 | 0.3880 | 16.790737 | 11.939017 |
| 2459817 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 8.184100 | -1.193739 | 0.071562 | -0.898306 | -0.745667 | -1.489977 | -0.635205 | -0.530998 | 0.8357 | 0.7232 | 0.4726 | 15.294653 | 17.380409 |
| 2459816 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 8.864484 | -1.257250 | 1.418474 | -1.229696 | 1.044804 | -0.123882 | 0.439381 | 1.797906 | 0.8534 | 0.6327 | 0.5520 | 13.914767 | 9.832704 |
| 2459815 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 7.706013 | -1.510710 | 0.930838 | -1.051218 | -0.109787 | -0.831083 | -0.574194 | 1.136299 | 0.8333 | 0.7293 | 0.4875 | 27.932917 | 30.260263 |
| 2459814 | RF_maintenance | 0.00% | - | - | - | - | - | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2459813 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 13.385392 | -2.191853 | -0.233142 | -1.037635 | 22.888005 | 1.359502 | 1.219071 | 2.258021 | 0.8137 | 0.7532 | 0.3848 | 25.665375 | 20.855486 |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 70 | N04 | RF_maintenance | ee Shape | 8.456627 | 8.456627 | -0.293421 | 0.865772 | -0.843292 | 3.496131 | 1.178002 | 0.315218 | -0.859387 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 70 | N04 | RF_maintenance | ee Shape | 7.800919 | 7.800919 | 0.008172 | -0.084279 | -0.442252 | 2.285607 | 0.667245 | 0.248455 | 0.252253 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 70 | N04 | RF_maintenance | ee Shape | 8.374646 | 8.374646 | -0.838765 | -0.166148 | -0.264930 | 3.684279 | 0.195774 | -0.248845 | 1.041498 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 70 | N04 | RF_maintenance | ee Shape | 9.893644 | 9.893644 | -0.951941 | 0.874587 | -0.114225 | 8.906623 | 0.423014 | 0.846833 | 0.187647 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 70 | N04 | RF_maintenance | ee Shape | 9.323621 | -0.672141 | 9.323621 | -1.143933 | 0.086023 | 0.632249 | 2.696610 | 0.214673 | -0.314586 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 70 | N04 | RF_maintenance | ee Shape | 10.038729 | -0.532019 | 10.038729 | -0.801615 | 0.607088 | 0.664998 | 0.913102 | -0.218355 | -0.478782 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 70 | N04 | RF_maintenance | ee Shape | 9.578967 | 9.578967 | -1.745728 | 0.553534 | -0.051104 | 3.132225 | 2.544959 | 0.002540 | 2.639137 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 70 | N04 | RF_maintenance | ee Temporal Variability | 12.502613 | -1.290675 | 11.820838 | -0.118307 | 0.268154 | 0.840959 | 12.502613 | 2.232043 | 0.673972 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 70 | N04 | RF_maintenance | ee Temporal Variability | 310.875066 | 60.469304 | 50.467234 | 97.760971 | 103.394563 | 310.875066 | 227.092493 | 48.965997 | 63.044520 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 70 | N04 | RF_maintenance | nn Temporal Variability | 81.109984 | -0.252056 | 8.895978 | -0.977040 | 0.098134 | 81.109984 | 73.978875 | 0.685384 | 0.964184 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 70 | N04 | RF_maintenance | ee Shape | 59.868823 | 59.868823 | -0.646397 | 15.423634 | -0.389525 | 9.168082 | 0.158602 | 22.480464 | 0.569161 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 70 | N04 | RF_maintenance | ee Power | 36.721142 | -1.114608 | 15.189952 | -0.432942 | 36.721142 | -1.438023 | 2.622069 | 0.786408 | 31.920818 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 70 | N04 | RF_maintenance | ee Temporal Discontinuties | 1152.780386 | 95.009380 | 80.276665 | 64.375493 | 83.557078 | 60.179840 | 109.401472 | 367.310189 | 1152.780386 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 70 | N04 | RF_maintenance | ee Temporal Variability | 48.368973 | 5.348576 | 5.715348 | 10.874766 | 9.445975 | 23.798494 | 48.368973 | -4.319967 | -1.700472 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 70 | N04 | RF_maintenance | ee Temporal Variability | 330.441792 | 19.046372 | 22.436812 | 29.594530 | 32.547052 | 316.639465 | 330.441792 | 41.956436 | 31.124191 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 70 | N04 | RF_maintenance | ee Shape | 11.622349 | 11.622349 | -1.751379 | 1.982080 | -0.870516 | 3.747527 | 3.292810 | 1.244831 | 4.685262 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 70 | N04 | RF_maintenance | ee Power | 39.654066 | 15.674380 | 0.602958 | 39.654066 | -0.598132 | 2.723431 | -1.173532 | 23.496090 | 0.182202 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 70 | N04 | RF_maintenance | ee Shape | 11.311974 | 11.311974 | -1.756946 | 1.161567 | -1.114440 | 1.352190 | 0.776556 | 0.031375 | 2.720704 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 70 | N04 | RF_maintenance | ee Shape | 14.999299 | -2.182727 | 14.999299 | -0.834072 | -0.266502 | 1.853197 | 10.225175 | 3.442189 | 1.766844 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 70 | N04 | RF_maintenance | nn Temporal Discontinuties | 12.159684 | -1.088280 | 10.352550 | -0.480246 | -0.199719 | 2.344704 | 0.952293 | 12.159684 | 0.077749 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 70 | N04 | RF_maintenance | ee Shape | 11.764318 | 11.764318 | -1.454700 | 0.611002 | -0.968971 | 2.609994 | 2.265415 | -0.281667 | -0.118386 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 70 | N04 | RF_maintenance | ee Shape | 8.596313 | -0.987261 | 8.596313 | -0.616144 | 0.824853 | -0.999831 | 3.393499 | 6.851849 | 1.135454 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 70 | N04 | RF_maintenance | ee Shape | 9.140665 | -1.248403 | 9.140665 | -0.992336 | 1.045872 | 0.857089 | -0.908615 | -0.607561 | -0.819655 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 70 | N04 | RF_maintenance | ee Shape | 9.340714 | 9.340714 | -1.812851 | 0.779806 | -0.986517 | 2.317545 | -0.906943 | 0.230268 | 0.860818 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 70 | N04 | RF_maintenance | ee Shape | 7.203718 | -1.398972 | 7.203718 | -0.452326 | 1.485308 | -0.939514 | 0.479001 | 1.893036 | -0.679824 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 70 | N04 | RF_maintenance | ee Shape | 8.841712 | 8.841712 | -1.063345 | 0.474672 | -1.021649 | -0.656194 | -1.243257 | 0.627963 | -0.218445 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 70 | N04 | RF_maintenance | ee Shape | 9.925905 | -0.946732 | 9.925905 | -1.024131 | 0.373186 | -0.961556 | -1.203158 | -0.566188 | 1.661898 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 70 | N04 | RF_maintenance | ee Temporal Variability | 20.355922 | 11.353067 | -1.473501 | -0.124232 | -0.860073 | 20.355922 | 4.018431 | 1.242641 | 0.764547 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 70 | N04 | RF_maintenance | ee Shape | 8.184100 | 8.184100 | -1.193739 | 0.071562 | -0.898306 | -0.745667 | -1.489977 | -0.635205 | -0.530998 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 70 | N04 | RF_maintenance | ee Shape | 8.864484 | -1.257250 | 8.864484 | -1.229696 | 1.418474 | -0.123882 | 1.044804 | 1.797906 | 0.439381 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 70 | N04 | RF_maintenance | ee Shape | 7.706013 | -1.510710 | 7.706013 | -1.051218 | 0.930838 | -0.831083 | -0.109787 | 1.136299 | -0.574194 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 70 | N04 | RF_maintenance | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 70 | N04 | RF_maintenance | ee Temporal Variability | 22.888005 | -2.191853 | 13.385392 | -1.037635 | -0.233142 | 1.359502 | 22.888005 | 2.258021 | 1.219071 |